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With large databases of document images available,a method for users to find keywords in documents will be useful. One approach is to perform Optical Character Recognition (OCR) on each document followed by indexing of the resulting text. However, if the quality of the document is poor or time is critical,complete OCR
In this paper we present a novel descriptor and method for segmentation-based keyword spotting. We introduce Zoning-Aggregated Hypercolumn features as pixel-level cues for document images. Motivated by recent research in machine vision, we use an appropriately pretrained convolutional network as a feature extraction
The Bag-of-Visual-Words (BoVW) approach has been attracted some attention in the field of keyword spotting. However, the BoVW approach discards the spatial relations of the visual words. Therefore, a visual language model is integrated into the BoVW framework in this study so as to add the spatial information. To
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